SoroushVahidi/scidocs huggingface dataset:
Certificates of virginity and reconstruction of the hymen.
(2008)

Virginity in women has traditionally been associated with the integrity of the hymen, the incomplete membrane occluding the lower end of the vagina. The hymen is often ruptured during the first coitus, which may cause bleeding of varying intensity. In certain communities, the bed-sheet, now soiled with blood, is shown as proof that the first sexual intercourse has taken place and that the bride was indeed a virgin up to that point. Thus, the sullied sheet constitutes evidence that the ‘honour’ of the woman and that of her family remain intact. Literature data concerning the frequency of bleeding at defloration are amazingly scarce. Olga Loeber conducted a retrospective survey among women of diverse ethnic origin and with different cultural and religious background aborted at one centre in The Netherlands. Somewhat less than half of these women reported that they had not bled during their first coitus! The Jewish, Christian and Muslim faiths all attach considerable importance to premarital virginity, particularly that of women. Yet, the social valorization of virginity dates back to a much earlier time than the imposition of chastity upon unmarried women in Muslim communities. Neither the Koran nor Hadiths state that virginity is a precondition to marriage. Patriarchal societies aim to control the sexuality of women in order to regulate lines of descent and transfer. Islam has only translated a social into a religious norm. It is worth remembering that until the 1960s, moral and behavioural norms kept even European atheists from engaging in premarital sex. These constraints have only been gradually lifted in the last 50 years as a result of the liberalization of sexuality and the improved social status of women.
Systematic review of bankruptcy prediction models: Towards a framework for tool selection
(2018)

The bankruptcy prediction research domain continues to evolve with many new different predictive models developed using various tools. Yet many of the tools are used with the wrong data conditions or for the wrong situation. Using the Web of Science, Business Source Complete and Engineering Village databases, a systematic review of 49 journal articles published between 2010 and 2015 was carried out. This review shows how eight popular and promising tools perform based on 13 key criteria within the bankruptcy prediction models research area. These tools include two statistical tools: multiple discriminant analysis and Logistic regression; and six artificial intelligence tools: artificial neural network, support vector machines, rough sets, case based reasoning, decision tree and genetic algorithm. The 13 criteria identified include accuracy, result transparency, fully deterministic output, data size capability, data dispersion, variable selection method required, variable types applicable, and more. Overall, it was found that no single tool is predominantly better than other tools in relation to the 13 identified criteria. A tabular and a diagrammatic framework are provided as guidelines for the selection of tools that best fit different situations. It is concluded that an overall better performance model can only be found by informed integration of tools to form a hybrid model. This paper contributes towards a thorough understanding of the features of the tools used to develop bankruptcy prediction models and their related shortcomings.
Impaired face processing in autism: fact or artifact?
(2006)

Within the last 10 years, there has been an upsurge of interest in face processing abilities in autism which has generated a proliferation of new empirical demonstrations employing a variety of measuring techniques. Observably atypical social behaviors early in the development of children with autism have led to the contention that autism is a condition where the processing of social information, particularly faces, is impaired. While several empirical sources of evidence lend support to this hypothesis, others suggest that there are conditions under which autistic individuals do not differ from typically developing persons. The present paper reviews this bulk of empirical evidence, and concludes that the versatility and abilities of face processing in persons with autism have been underestimated.
A Variable Threshold Voltage CMOS Comparator for Flash Analog to Digital Converter
(2014)

This paper presents a variable threshold voltage CMOS comparator for flash analog to digital converter. The proposed comparator has single-ended type of architecture. The comparator is designed and analyzed by Cadence Virtuoso Analog Design Environment using UMC 180nm technology. The proposed comparator consumes peak power of 34. 97 ?W from 1. 8 V power supply. It achieves the power delay product (PDP) of 8 fJ and propagation delay of 230 ps. The designed comparator eliminates the requirement of resistive ladder network for reference voltage generation. This makes it highly suitable in the design of flash analog to digital converter.
Automatic Registration Method for Optical Remote Sensing Images with Large Background Variations Using Line Segments
(2016)

Image registration is an essential step in the process of image fusion, environment surveillance and change detection. Finding correct feature matches during the registration process proves to be difficult, especially for remote sensing images with large background variations (e.g., images taken pre and post an earthquake or flood). Traditional registration methods based on local intensity probably cannot maintain steady performances, as differences are significant in the same area of the corresponding images, and ground control points are not always available in many disaster images. In this paper, an automatic image registration method based on the line segments on the main shape contours (e.g., coastal lines, long roads and mountain ridges) is proposed for remote sensing images with large background variations because the main shape contours can hold relatively more invariant information. First, a line segment detector called EDLines (Edge Drawing Lines), which was proposed by Akinlar et al. in 2011, is used to extract line segments from two corresponding images, and a line validation step is performed to remove meaningless and fragmented line segments. Then, a novel line segment descriptor with a new histogram binning strategy, which is robust to global geometrical distortions, is generated for each line segment based on the geometrical relationships,including both the locations and orientations of theremaining line segments relative to it. As a result of the invariance of the main shape contours, correct line segment matches will have similar descriptors and can be obtained by cross-matching among the descriptors. Finally, a spatial consistency measure is used to remove incorrect matches, and transformation parameters between the reference and sensed images can be figured out. Experiments with images from different types of satellite datasets, such as Landsat7, QuickBird, WorldView, and so on, demonstrate that the proposed algorithm is automatic, fast (4 ms faster than the second fastest method, i.e., the rotationand scale-invariant shape context) and can achieve a recall of 79.7%, a precision of 89.1% and a root mean square error (RMSE) of 1.0 pixels on average for remote sensing images with large background variations.
Sentence-State LSTM for Text Representation
(2018)

The baseline BiLSTM model consists of two LSTM components, which process the input in the forward left-to-right and the backward rightto-left directions, respectively. In each direction, the reading of input words is modelled as a recurrent process with a single hidden state. Given an initial value, the state changes its value recurrently, each time consuming an incoming word. Take the forward LSTM component for example. Denoting the initial state as h 0, which is a model parameter, the recurrent state transition step for calculating h 1, . . . , h n+1 is defined as follows (Graves and Schmidhuber, 2005):
The causes of welfare state expansion : deindustrialization or globalization ?
(1998)

An influential line of argument holds that trade exposure causes economic uncertainty and spurs popular demands for compensatory and risk-sharing welfare state spending. The argument has gained renewed prominence through the recent work of Garrett (1998) and Rodrik (1997; 1998). This paper argues that the relationship between trade openness and welfare state expansion is spurious, and that the engine of welfare state expansion since the 1960s has been deindustrialization. Based on cross-sectional time-series data for 15 OECD countries we show that there is no relationship between trade exposure and the level of labor market risks (in terms of employment and wages), whereas the uncertainty and dislocations caused by deindustrialization have spurred electoral demands for compensating welfare state policies. Yet, while differential rates of deindustrialization explain differences in the overall size of the welfare state, its particular character -in terms of the share of direct government provision and the equality of transfer payments -is shaped by government partisanship. The argument has implications for the study, and the future,of thewelfare state thatarevery different from thosesuggested in the trade openness literature. Zusammenfassung In vielen einflußreichen Diskussionsbeiträgen wird die Meinung vertreten, daß die Liberalisierung des Handels ökonomische Verunsicherung zur Folge habe und damit zu Forderungen nach ausgleichenden wohlfahrtsstaatlichen Ausgaben führe. Die Arbeiten von Garrett (1998) und Rodrik (1997;1998) verliehen diesem Argument zusätzliche Relevanz. Gegenstand dieser Untersuchung ist die Beziehung zwischen Ausmaß an Offenheit einer Volkswirtschaft und der Ausdehnung des Wohlfahrtsstaates, dessen großzügige Entwicklung seit den 1960er Jahren durch zunehmende Deindustrialisierung ermöglicht wurde. Auf der Grundlage von Analysen länderübergreifender Zeitreihen und von 15 OECD-Ländern wird gezeigt, daß kein Zusammenhang zwischen einer Handelsliberalisierung und dem Grad der Arbeitsmarktrisiken (bezogen auf Löhne und Beschäftigung) besteht. Angesichts der durch die Deindustrialisierung verursachten Unsicherheit kommt es jedoch von seiten der Wähler zu Forderungen nach einer ausgleichenden Sozialpolitik. Während das Ausmaß der Deindustrialisierung die Größe und Ausstattung des Wohlfahrsstaates determiniert, wird sein spezifischer Charakter hinsichtlich der direkten Regierungsdienstleistungen und der ausgleichenden Transferzahlungen von den Regierungsparteien geprägt. Diese Argumentation ist von großer Tragweite für die Analyse und Zukunft des Wohlfahrtsstaates; sie weicht gravierend von der Literatur über offene Volkswirtschaften ab.
A Tutorial on Reinforcement Learning Techniques
(1999)

Reinforcement Learning (RL) is learning through direct experimentation. It does not assume the existence of a teacher that provides examples upon which learning of a task takes place. Instead, in RL experience is the only teacher. With historical roots on the study of conditioned reflexes, RL soon attracted the interest of Engineers and Computer Scientists because of its theoretical relevance and potential applications in fields as diverse as Operational Research and Robotics. Computationally, RL is intended to operate in a learning environment composed by two subjects: the learner and a dynamic process. At successive time steps, the learner makes an observation of the process state, selects an action and applies it back to the process. The goal of the learner is to find out an action policy that controls the behavior of this dynamic process, guided by signals (reinforcements) that indicate how well it is performing the required task. These signals are usually associated to some dramatic condition — e.g., accomplishment of a subtask (reward) or complete failure (punishment), and the learner’s goal is to optimize its behavior based on some performance measure (a function of the received reinforcements). The crucial point is that in order to do that, the learner must evaluate the conditions (associations between observed states and chosen actions) that lead to rewards or punishments. In other words, it must learn how to assign credit to past actions and states by correctly estimating costs associated to these events. Starting from basic concepts, this tutorial presents the many flavors of RL algorithms, develops the corresponding mathematical tools, assess their practical limitations and discusses alternatives that have been proposed for applying RL to realistic tasks, such as those involving large state spaces or partial observability. It relies on examples and diagrams to illustrate the main points, and provides many references to the specialized literature and to Internet sites where relevant demos and additional information can be obtained.
Robust Influence Maximization
(2016)

In this paper, we address the important issue of uncertainty in the edge influence probability estimates for the well studied influence maximization problem --- the task of finding k seed nodes in a social network to maximize the influence spread. We propose the problem of robust influence maximization, which maximizes the worst-case ratio between the influence spread of the chosen seed set and the optimal seed set, given the uncertainty of the parameter input. We design an algorithm that solves this problem with a solution-dependent bound. We further study uniform sampling and adaptive sampling methods to effectively reduce the uncertainty on parameters and improve the robustness of the influence maximization task. Our empirical results show that parameter uncertainty may greatly affect influence maximization performance and prior studies that learned influence probabilities could lead to poor performance in robust influence maximization due to relatively large uncertainty in parameter estimates, and information cascade based adaptive sampling method may be an effective way to improve the robustness of influence maximization.
An fMRI investigation of emotional engagement in moral judgment.
(2001)

The long-standing rationalist tradition in moral psychology emphasizes the role of reason in moral judgment. A more recent trend places increased emphasis on emotion. Although both reason and emotion are likely to play important roles in moral judgment, relatively little is known about their neural correlates, the nature of their interaction, and the factors that modulate their respective behavioral influences in the context of moral judgment. In two functional magnetic resonance imaging (fMRI) studies using moral dilemmas as probes, we apply the methods of cognitive neuroscience to the study of moral judgment. We argue that moral dilemmas vary systematically in the extent to which they engage emotional processing and that these variations in emotional engagement influence moral judgment. These results may shed light on some puzzling patterns in moral judgment observed by contemporary philosophers.
IoT based Smart Home design using power and security management
(2016)

The paper presents the design and implementation of an Ethernet-based Smart Home intelligent system for monitoring the electrical energy consumption based upon the real time tracking of the devices at home an INTEL GALILEO 2ND generation development board, which can be used in homes and societies. The proposed system works on real time monitoring and voice control, so that the electrical devices and switches can be remotely controlled and monitored with or without an android based app. It uses various sensors to not only monitor the real time device tracking but also maintaining the security of your house. It is monitored and controlled remotely from an android app using the Internet or the Intranet connectivity. The proposed outcome of the project aims as multiple benefits of saving on electricity bills of the home as well as keep the users updated about their home security with an option of controlling the switching of the devices by using their voice or simple toggle touch on their smartphone, and last but most importantly, monitor the usage in order to conserve the precious natural resources by reducing electrical energy consumption.
Scalable Visualisation of Sentiment and Stance
(2018)

Natural language processing systems have the ability to analyse not only the sentiment of human language, but also the stance of the speaker. Representing this information visually from unevenly distributed and potentially sparse datasets is challenging, in particular when trying to facilitate exploration and knowledge discovery. We present work on a novel visualisation approach for scalable visualisation of sentiment and stance and provide a language resource of e-government public engagement of 9,278 user comments with stance explicitly declared by the author.
Differentially Private K-Means Clustering
(2016)

There are two broad approaches for differentially private data analysis. The interactive approach aims at developing customized differentially private algorithms for various data mining tasks. The non-interactive approach aims at developing differentially private algorithms that can output a synopsis of the input dataset, which can then be used to support various data mining tasks. In this paper we study the effectiveness of the two approaches on differentially private k-means clustering. We develop techniques to analyze the empirical error behaviors of the existing interactive and non-interactive approaches. Based on the analysis, we propose an improvement of DPLloyd which is a differentially private version of the Lloyd algorithm. We also propose a non-interactive approach EUGkM which publishes a differentially private synopsis for k-means clustering. Results from extensive and systematic experiments support our analysis and demonstrate the effectiveness of our improvement on DPLloyd and the proposed EUGkM algorithm.
{31 () Unifying Instance-based and Rule-based Induction Pedro Domingos
(1996)

Several well-developed approaches to inductive learning now exist, but each has speci c limitations that are hard to overcome. Multi-strategy learning attempts to tackle this problem by combining multiple methods in one algorithm. This article describes a uni cation of two widely-used empirical approaches: rule induction and instance-based learning. In the new algorithm, instances are treated as maximally speci c rules, and classi cation is performed using a best-match strategy. Rules are learned by gradually generalizing instances until no improvement in apparent accuracy is obtained. Theoretical analysis shows this approach to be e cient. It is implemented in the RISE 3.1 system. In an extensive empirical study, RISE consistently achieves higher accuracies than state-of-the-art representatives of both its parent approaches (PEBLS and CN2), as well as a decision tree learner (C4.5). Lesion studies show that each of RISE's components is essential to this performance. Most signi cantly, in 14 of the 30 domains studied, RISE is more accurate than the best of PEBLS and CN2, showing that a signi cant synergy can be obtained by combining multiple empirical methods.
Benchmarking short text semantic similarity
(2010)

Short Text Semantic Similarity measurement is a ne w and rapidly growing field of research. “Short texts” are typically sentence leng th but are not required to be grammatically correct . There is great potential for applying these measure s in fields such as Information Retrieval, Dialogue Management and Question Answering. A datas et of 65 sentence pairs, with similarity ratings, produced in 2006 has become adopted as a de facto Gold Standard benchmark. This paper discusses the adoption of the 2006 dataset, lays do wn a number of criteria that can be used to determine whether a dataset should be awarded a “Go ld Standard” accolade and illustrates its use as a benchmark. Procedures for the generation of furth er Gold Standard datasets in this field are recommended.
01 05 03 2 v 2 1 5 N ov 2 00 1 Quantum Digital Signatures
(2001)

We present a quantum digital signature scheme whose security is based on fundamental principles of quantum physics. It allows a sender (Alice) to sign a message in such a way that the signature can be validated by a number of different people, and all will agree either that the message came from Alice or that it has been tampered with. To accomplish this task, each recipient of the message must have a copy of Alice’s “public key,” which is a set of quantum states whose exact identity is known only to Alice. Quantum public keys are more difficult to deal with than classical public keys: for instance, only a limited number of copies can be in circulation, or the scheme becomes insecure. However, in exchange for this price, we achieve unconditionally secure digital signatures. Sending an m-bit message uses up O(m) quantum bits for each recipient of the public key. We briefly discuss how to securely distribute quantum public keys, and show the signature scheme is absolutely secure using one method of key distribution. The protocol provides a model for importing the ideas of classical public key cryptography into the quantum world.
Medieval Islamic Architecture , Quasicrystals , and Penrose and Girih Tiles : Questions from the Classroom
(2013)

Tiling Theory studies how one might cover the plane with various shapes. Medieval Islamic artisans developed intricate geometric tilings to decorate their mosques, mausoleums, and shrines. Some of these patterns, called girih tilings, first appeared in the 12 Century AD. Recent investigations show these medieval tilings contain symmetries similar to those found in aperiodic Penrose tilings first investigated in the West in the 1970’s. These intriguing discoveries may suggest that the mathematical understanding of these artisans was much deeper than originally thought.
An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition
(1999)

ÐThis paper describes a hidden Markov model-based approach designed to recognize off-line unconstrained handwritten words for large vocabularies. After preprocessing, a word image is segmented into letters or pseudoletters and represented by two feature sequences of equal length, each consisting of an alternating sequence of shape-symbols and segmentationsymbols, which are both explicitly modeled. The word model is made up of the concatenation of appropriate letter models consisting of elementary HMMs and an HMM-based interpolation technique is used to optimally combine the two feature sets. Two rejection mechanisms are considered depending on whether or not the word image is guaranteed to belong to the lexicon. Experiments carried out on real-life data show that the proposed approach can be successfully used for handwritten word recognition. Index TermsÐHandwriting modeling, preprocessing, segmentation, feature extraction, hidden Markov models, word recognition, rejection.
A Full English Sentence Database for Off-line Handwriting Recognition
(1999)

In this paper we present a new database for off-line handwriting recognition, together with a few preprocessing and text segmentation procedures. The database is based on the Lancaster-Oslo/Bergen(LOB) corpus. This corpus is a collection of texts that were used to generate forms, which subsequently were filled out by persons with their handwriting. Up to now (December 1998) the database includes 556 forms produced by approximately 250 different writers. The database consists of full English sentences. It can serve as a basis for a variety of handwriting recognition tasks. The main focus, however, is on recognition techniques that use linguistic knowledge beyond the lexicon level. This knowledge can be automatically derived from the corpus or it can be supplied from external sources.